Within the domain of deep learning methods, [1]} used a network that operates on predefined audio and visual features, in contrast to our work where we predict a shift for each frame and learn the visual features directly. Chung et al. used a CNN model called SyncNet to learn visual features directly from the video stream, and audio features from the MFCC representation of the audio [2]}. They then find the optimal global shift by computing the contrastive loss over all frames. In contrast, we employ an alignment network and are not limited to a global shift.
